Adding Concept Learning to Neural Networks

Linking logic and deep learning,  a great idea.   Did it in the 80s to add analytics and learning to expert systems.   Still not enough support to make it work well.

Deep learning is about to get easier — and more widespread in Venturebeat   By Ben Dickson, Techtalks n @BEBDEE983

(excerpt)

" ... In a paper presented at the ICLR conference in May, researchers from MIT and IBM introduced the “Neuro-Symbolic Concept Learner ", an AI model that brings together rule-based AI and neural networks.

NSCL uses neural networks to extract features from images and compose a structured table of information (called “symbols” in AI jargon). It then uses a classic, rule-based program to answer questions and solve problems based on those symbols.

By combining the learning capabilities of neural nets and the reasoning power of rule-based AI, NSCL can adapt to new settings and problems with much less data. The researchers tested the AI model on CLEVR, a dataset for solving visual question answering (VQA). In VQA, an AI must answer questions about the objects and elements contained in a given picture.

AI models based purely on neural networks usually need a lot of training examples to solve VQA problems with decent accuracy. However, NSCL was able to master CLEVR with a fraction of the data.  ... "